Seedling asset evidence storage method, electronic equipment and storage medium

Abstract

The invention provides a nursery stock asset evidence storage method, electronic equipment and a storage medium, and belongs to the technical field of smart gardens. The method comprises the steps that multi-source heterogeneous data of nursery stocks are collected, multiple original feature values of different dimensions are extracted from the multi-source heterogeneous data, and the original feature values are used for quantitatively representing the physiological growth state of the nursery stocks; based on the uncertainty metric value of each original feature value in the current time period, calculating to obtain a confidence weight of each original feature value; based on the confidence weight of each original feature value, performing weighted fusion on the plurality of original feature values to obtain a fused feature value; and generating an integrity commitment hash corresponding to the original feature value and an evidence storage hash corresponding to the fusion feature value, forming an evidence storage transaction by the integrity commitment hash and the evidence storage hash, and recording the evidence storage transaction on the block chain. According to the method, the authenticity and reliability of uplink data can be ensured from the source, the on-chain storage cost is greatly reduced, and credible, efficient and low-cost nursery stock asset datamation management is realized.

Description

Technical Field

[0001] This application relates to the field of smart landscaping technology, and in particular to a method for storing seedling assets, electronic equipment, and storage medium. Background Technology

[0002] With the advancement of smart city and digital management of ecological assets, the demand for precise and reliable digital management of the growth status of garden seedlings, as both components of the urban ecosystem and high-value living assets, is becoming increasingly prominent. Blockchain technology, due to its distributed, immutable, and traceable characteristics, is considered an ideal cornerstone for constructing digital identities and asset certificates for seedlings. Theoretically, it can provide reliable data support for high-end commercial scenarios such as seedling property rights transactions, mortgage financing, carbon sequestration measurement, and insurance claims.

[0003] Currently, existing technologies include solutions for applying blockchain to agricultural product traceability (such as CN120278737A, which discloses a blockchain-based method for full-process traceability of rural agricultural products). These solutions typically focus on tracing and storing process information and final quality data of agricultural products (such as grains, fruits, and vegetables) during planting, processing, and distribution. The technical approach primarily involves collecting environmental and process data through IoT devices, compressing it, and then uploading it to the blockchain to ensure data integrity and immutability during transmission. The ultimate goal is to provide consumers with origin traceability and quality verification. However, this method, centered on "agricultural product traceability," reveals fundamental inapplicability and numerous technical flaws when simply applied to the specific scenario of "landscape seedlings."

[0004] Firstly, agricultural product traceability focuses on relatively static process records and the quality attributes of the final product. However, horticultural seedlings, as continuously growing living assets, have their core value in their dynamic growth status and health. Existing traceability technologies directly record raw sensor readings or simply aggregate data. However, sensors are susceptible to environmental interference and equipment aging, leading to data distortion. Blockchain can only guarantee that "distorted data" is not tampered with, but it cannot guarantee the authenticity and accuracy of the data from the source.

[0005] Secondly, seedling growth monitoring is a high-frequency process that lasts for months or even years, especially given the massive amount of data generated by high-resolution remote sensing and lidar point cloud data. Even with compression, existing solutions still incur huge storage and transaction costs if all key time-series feature data is uploaded to the blockchain, making it difficult to support the long-term, high-frequency monitoring needs of large-scale seedling assets and thus lacking economic feasibility.

[0006] Therefore, there is an urgent need to propose an asset preservation method specifically designed for the characteristics of living garden seedlings to solve the above problems. Summary of the Invention

[0007] The purpose of this application is to provide a method, electronic device, and storage medium for storing seedling assets in order to solve the above-mentioned problems.

[0008] To achieve the above objectives, firstly, this application proposes a method for storing seedling assets, which includes: Collect multi-source heterogeneous data of seedlings and extract multiple original feature values ​​of different dimensions from the multi-source heterogeneous data. The original feature values ​​are used to quantitatively characterize the physiological growth status of seedlings. Based on the uncertainty measure of each original feature value in the current time period, the confidence weight of each original feature value is calculated. Based on the confidence weights of each original feature value, multiple original feature values ​​are weighted and fused to obtain a fused feature value. Generate the integrity commitment hash corresponding to the original feature value and the evidence storage hash corresponding to the fused feature value, and record the evidence storage transaction on the blockchain using the integrity commitment hash and the evidence storage hash.

[0009] In some implementations, the multi-source heterogeneous data includes satellite remote sensing data, ground sensor data, and UAV lidar point cloud data. The extraction of multiple original feature values ​​of different dimensions from the multi-source heterogeneous data, used to quantitatively characterize the physiological growth state of the seedlings, includes: Near-infrared and red light reflectance are extracted from the satellite remote sensing data, and a photosynthetic activity index is calculated based on the near-infrared and red light reflectance. The photosynthetic activity index is used to characterize the photosynthetic activity of seedlings. A set of parameters including plant canopy temperature, air temperature, humidity and soil electrical conductivity is extracted from the ground sensor data, and a water stress index is calculated based on the parameter set. The water stress index is used to characterize the water status of seedlings. The three-dimensional spatial structure information of the seedlings is extracted from the point cloud data of the UAV lidar, and the morphological formation index is calculated based on the three-dimensional spatial structure information. The morphological formation index is used to characterize the morphological growth vitality of the seedlings. In some implementations, before calculating the confidence weight of each original feature value based on the uncertainty metric of each original feature value in the current time period, the following steps are included: Obtain the time series data of each original feature value within the current time period, and calculate the corresponding information entropy and standard deviation based on the time series data; Based on the ratio of information entropy to standard deviation corresponding to each original feature value, the uncertainty measure of each original feature value in the current time period is calculated. In some implementations, calculating the confidence weight of each original feature value based on the uncertainty metric of each original feature value in the current time period includes: The confidence weight of each original feature value is calculated based on the uncertainty measure of each original feature value in the current time period and the sum of the reciprocals of the squares of the uncertainty measures of all original feature values ​​in the current time period. In some implementations, the weighted fusion of multiple original feature values ​​based on their confidence weights to obtain fused feature values ​​includes: Each original feature value is multiplied by its corresponding confidence weight. The original feature values ​​include the photosynthetic activity index, which characterizes the photosynthetic activity of seedlings; the water stress index, which characterizes the water status of seedlings; and the morphogenesis index, which characterizes the morphological growth activity of seedlings. The product of the photosynthetic activity index, the negative value of the product of the water stress index, and the product of the morphogenesis index are summed to obtain the fusion characteristic value. The range of the fusion characteristic value is then limited to the interval (-1, 1) by the hyperbolic tangent function. In some implementations, generating the integrity commitment hash corresponding to the original feature value and the evidence storage hash corresponding to the fused feature value includes: All original feature values, corresponding confidence weights, and timestamps are serialized, and hash values ​​are calculated on the serialized data to generate an integrity commitment hash for multi-source heterogeneous data; and The fused feature value is amplified and rounded, then combined with the timestamp, and the combined data is hashed to generate the evidence hash. In some embodiments, the method further includes: The multi-source heterogeneous data is stored in a distributed storage system, and the content identifier corresponding to the multi-source heterogeneous data in the distributed storage system is determined. The step of recording the integrity commitment hash and the evidence storage hash as evidence storage transactions on the blockchain includes: The integrity commitment hash, the evidence storage hash, and the content identifier are used to form an evidence storage transaction record on the blockchain. In some embodiments, the method further includes: In response to a business verification request from a verifier, a zero-knowledge proof is generated on a private blockchain based on the original feature values ​​as private inputs using a proof generation algorithm. The proof circuit corresponding to the proof generation algorithm is configured to generate a zero-knowledge proof when the integrity commitment hash and fused feature value calculated from the private inputs are consistent with the integrity commitment hash and fused feature value stored on the blockchain. The zero-knowledge proof is used to enable the verifier to verify the business verification request on a public blockchain.

[0010] Secondly, to achieve the above objectives, this application also proposes an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the seedling asset storage method described above.

[0011] Thirdly, to achieve the above objectives, this application also proposes a computer storage medium storing executable instructions, which, when executed by a processor, cause the processor to execute the seedling asset preservation method described above.

[0012] Compared with the prior art, the beneficial effects of this application include: Firstly, this application collects multi-source heterogeneous data on seedlings and extracts multiple original feature values ​​from them, transforming the raw, physical-level sensor data into original feature values ​​that can directly quantify the physiological growth state of the seedlings. Furthermore, it introduces dynamic confidence weights based on uncertainty measurement to weight and fuse the original feature values. This effectively suppresses distortion or anomalies caused by single data sources due to factors such as equipment aging and environmental interference, dynamically filtering and fusing the most reliable data sources to output a highly reliable fused feature value. This ensures that the object stored on the blockchain is high-quality information that has undergone reliable processing, rather than raw, unreliable readings, thus guaranteeing the authenticity and accuracy of the data from the source.

[0013] Secondly, this application reduces the amount of data stored on-chain by several orders of magnitude by generating and uploading lightweight double-hash data (integrity commitment hash and evidence storage hash), greatly saving storage costs and network resources. This effectively solves the technical problems of high on-chain storage costs and low data processing efficiency caused by uploading all data to the blockchain in existing technologies. Simultaneously, the collision-free nature of cryptographic hashes ensures that any tampering with off-chain data can be effectively detected, achieving a balance between low cost and high reliability. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation on the scope of this application.

[0015] Figure 1 This is a flowchart illustrating a method for storing seedling assets in one embodiment; Figure 2 This is a schematic diagram of multi-terminal interaction for a seedling asset preservation method in another embodiment; Figure 3 This is a schematic diagram of the electronic equipment involved in the seedling asset preservation method in this application embodiment. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] All terms used in this application (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0018] For example, the terms "first," "second," etc., used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For instance, without departing from the scope of this application, the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element. Both the first element and the second element are elements, but they are not the same element.

[0019] For example, the terms "comprising" or "including" used in this application indicate the presence of features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0020] As mentioned earlier, existing technologies include solutions for applying blockchain to agricultural product traceability (such as CN120278737A, which discloses a blockchain-based method for full-process traceability of rural agricultural products). These solutions typically focus on tracing and storing process information and final quality data of agricultural products (such as grains, fruits, and vegetables) during planting, processing, and distribution. The technical approach primarily involves collecting environmental and process data through IoT devices, compressing it, and uploading it to the blockchain to ensure data integrity and immutability during transmission. The ultimate goal is to provide consumers with origin traceability and quality verification. However, this method, centered on "agricultural product traceability," reveals fundamental inapplicability and numerous technical flaws when simply applied to the specific scenario of "landscape seedlings."

[0021] Firstly, agricultural product traceability focuses on relatively static process records and the quality attributes of the final product. However, horticultural seedlings, as continuously growing living assets, have their core value in their dynamic growth status and health. Existing traceability technologies directly record raw sensor readings or simply aggregate data. However, sensors are susceptible to environmental interference and equipment aging, leading to data distortion. Blockchain can only guarantee that "distorted data" is not tampered with, but it cannot guarantee the authenticity and accuracy of the data from the source.

[0022] Secondly, seedling growth monitoring is a high-frequency process that lasts for months or even years, especially given the massive amount of data generated by high-resolution remote sensing and lidar point cloud data. Even with compression, existing solutions still incur huge storage and transaction costs if all key time-series feature data is uploaded to the blockchain, making it difficult to support the long-term, high-frequency monitoring needs of large-scale seedling assets and thus lacking economic feasibility.

[0023] Therefore, there is an urgent need to propose an asset notarization method specifically designed for the characteristics of living garden seedlings to solve the above problems. To this end, this application proposes a seedling asset notarization method, electronic device, and storage medium. By using dynamic confidence weights to weight and fuse the original feature values, the authenticity and reliability of the on-chain data are ensured from the source. Furthermore, through a dual-hash on-chain notarization structure, the on-chain storage cost is significantly reduced while ensuring the auditability of all data, thus achieving reliable, efficient, and low-cost data management of seedling assets.

[0024] like Figure 1 As shown in the embodiment of this application, a method for storing seedling assets is provided, the method comprising the following steps: Step S10: Collect multi-source heterogeneous data of seedlings and extract multiple original feature values ​​of different dimensions from the multi-source heterogeneous data.

[0025] In this embodiment, multi-source heterogeneous data refers to data originating from different types of sensors or platforms, with varying data formats and physical meanings. Original feature values ​​refer to index parameters calculated based on multi-source heterogeneous data, capable of quantifying and characterizing the physiological growth status of seedlings in a specific aspect.

[0026] In some implementations, the multi-source heterogeneous data may include satellite remote sensing data (reflecting macroscopic spectral information), ground sensor data (reflecting location microenvironment information), and UAV lidar point cloud data (reflecting three-dimensional structural information). The raw feature value extracted from the satellite remote sensing data is the photosynthetic activity index (P), used to characterize the photosynthetic activity of seedlings. The raw feature value extracted from the ground sensor data is the water stress index (W), used to characterize the water status of seedlings. The raw feature value extracted from the UAV lidar point cloud data is the morphogenesis index (M), used to characterize the morphological growth activity of seedlings.

[0027] As a feasible implementation method for extracting the photosynthetic activity index (P) from satellite remote sensing data, near-infrared and red band reflectance can be extracted from the satellite remote sensing data, and the photosynthetic activity index can be calculated based on the near-infrared and red band reflectance. The specific calculation process is shown in formula (1): (1) in, Reflectivity in the near-infrared band (approximately 700-1300 nanometers), This is the real-time reflectance in the red light band (approximately 600-700 nanometers). This refers to the minimum reflectivity of the red light band within a specific time or area. This represents the maximum reflectivity of the red light band within a specific time or region.

[0028] It should be noted that healthy plant leaves strongly reflect near-infrared light. Therefore, the more lush and healthy the vegetation, the higher its near-infrared reflectivity. The more abundant and healthier the vegetation, the higher its red light reflectance. Plant chlorophyll strongly absorbs red light for photosynthesis. Therefore, the more lush and healthy the vegetation, the higher its red light reflectance. The lower the value, the better.

[0029] When vegetation is healthy, molecules It's very large, the denominator is... Very small, then The value will be very large. When the vegetation is unhealthy or sparse... It will decrease. It will rise, then The value will decrease. It can be used to reflect the biomass of seedlings and plants.

[0030] It's a regulating factor; if the current vegetation is healthy, near If the vegetation is sparse or withered, then the value of this adjustment term will be close to 0. near Then the value of this adjustment term will be close to 1. This adjustment term can be based on the relative position of the red light band reflectance mapped to the entire possible reflectance range. The biomass data is fine-tuned to make it more sensitive to the plant’s actual photosynthetic capacity (not just biomass).

[0031] also, While it reflects biomass, it reaches a saturation point when vegetation is very dense, and no longer increases linearly with increasing biomass. This can be determined by taking the natural logarithm. It can compress the data range, which helps to correct the signal saturation effect caused by excessive canopy density, and makes the index still sensitive to changes in high biomass areas.

[0032] Therefore, through formula (1), the photosynthetic activity index (P) becomes a comprehensive indicator that can dynamically, accurately and stably reflect the actual photosynthetic activity of seedlings under the current environment, and can capture the growth status of seedlings better than simple vegetation ratio indices (such as NDVI).

[0033] As a feasible implementation method for extracting the water stress index (W) from ground sensor data, a parameter set including plant canopy temperature, air temperature, humidity, and soil electrical conductivity can be extracted from the ground sensor data, and the water stress index can be calculated based on the parameter set. The larger the value of the water stress index, the greater the stress pressure on the seedlings due to water shortage. The specific calculation process is shown in formula (2): (2) in, Temperature of the plant canopy (°C). VPD represents air temperature (°C) and water vapor pressure difference (kPa). Soil electrical conductivity (mS / m).

[0034]

[0035] in, RH is the saturated vapor pressure (kPa) and relative humidity.

[0036]

[0037] Where T is the dry-bulb temperature of air (°C), and 0.6108, 17.27, and 237.3 are values ​​used to calculate the saturated vapor pressure (°C). The empirical coefficients of the empirical formula.

[0038] As a feasible implementation method for extracting the morphological formation index (M) from UAV lidar point cloud data, the three-dimensional spatial structure information of seedlings (including crown volume, crown projection area, crown volume change rate, etc.) can be extracted from the UAV lidar point cloud data, and the morphological formation index can be calculated based on the three-dimensional spatial structure information. The larger the value of the morphological formation index, the denser the growth structure of the seedling; the smaller the value, the sparser the growth structure of the seedling. The specific calculation process is shown in formula (3): (3) in, To obtain the point cloud density gradient, the UAV LiDAR point cloud data can be spatially meshed, the point cloud density within each voxel (3D pixel) can be calculated, and then its spatial gradient can be obtained. The volume of the tree canopy is (m³). The area is the canopy projection area (m²). This represents the rate of change of the tree canopy volume over time.

[0039]

[0040] in,( , Let be the coordinates of the i-th vertex of the convex hull.

[0041] Step S20: Calculate the confidence weight of each original feature value based on the uncertainty measure of each original feature value in the current time period.

[0042] In this embodiment, the uncertainty metric is a quantitative indicator used to evaluate the reliability and stability of each original feature value at the current moment. The higher the uncertainty metric, the less reliable the corresponding original feature value. Before step S20, time series data of each original feature value within the current time period can be obtained, and the corresponding information entropy and standard deviation can be calculated based on the time series data; based on the ratio of the information entropy and standard deviation of each original feature value, the uncertainty metric of each original feature value in the current time period can be calculated.

[0043] Specifically, time series data refers to a series of values ​​arranged chronologically within a given time period (e.g., the past 6 hours) representing a primary characteristic value, reflecting the dynamic changes of that primary characteristic value within a specific time window. Information entropy is an indicator used to quantify the disorder or unpredictability of time series data within its range of values. A higher entropy value indicates a more disordered and unpredictable data sequence. Standard deviation is a statistical measure of data dispersion, reflecting the magnitude of fluctuation in time series data relative to its mean. A larger standard deviation indicates more volatile data.

[0044] make Let X be the information entropy of a certain original feature value. Let X be the standard deviation of a given original feature value. Then the uncertainty measure is... .

[0045] in, ; .

[0046] In the formula, The original feature value X takes the value of The probability, This represents the mean of the original feature value X over the current time period.

[0047] The confidence weight is a value between 0 and 1, representing the weight of the corresponding original feature value during the fusion process. The higher the weight, the more reliable the corresponding original feature value.

[0048] Specifically, the confidence weight of each original feature value can be calculated based on the uncertainty measure of each original feature value in the current time period and the sum of the reciprocals of the squares of the uncertainty measures of all original feature values ​​in the current time period.

[0049] For example, the original characteristic values ​​include the photosynthetic activity index (P), the water stress index (W), and the morphogenesis index (M). Then, the uncertainty measure of the photosynthetic activity index (P) within the current time period is... The uncertainty measure of the water stress index (W) in the current time period is: The uncertainty measure of the morphological formation index (M) in the current time period is: .

[0050] Confidence weight of photosynthetic activity index (P) .

[0051] Confidence weights of the water stress index (W) .

[0052] Confidence weights of the morphology formation index (M) .

[0053] Step S30: Based on the confidence weights of each original feature value, the multiple original feature values ​​are weighted and fused to obtain the fused feature value.

[0054] In this embodiment, the fusion feature value refers to the core status indicator used to comprehensively reflect the seedling growth status information from multiple dimensions.

[0055] Specifically, each original feature value is multiplied by its corresponding confidence weight. The original feature values ​​include the photosynthetic activity index (P) used to characterize the photosynthetic activity of seedlings, the water stress index (W) used to characterize the water status of seedlings, and the morphogenesis index (M) used to characterize the morphological growth activity of seedlings. The product of the photosynthetic activity index, the negative value of the product of the water stress index, and the product of the morphogenesis index are summed to obtain the fused feature value. The range of the fused feature value is then limited to the interval (-1, 1) by the hyperbolic tangent function.

[0056] For example, fused feature values .

[0057] It should be noted that the reason for taking a negative value for the product of the water stress indices is that the water stress index (W) is a negative feature; a larger value indicates a worse water condition and poorer growth status of the seedlings. Taking a negative value during fusion helps to align it with other positively correlated features (W). It maintains the same direction as the morphology formation index.

[0058] Furthermore, by employing the hyperbolic tangent function By limiting the range of fused feature values ​​to the interval (-1, 1), The closer the value is to 1, the better the seedling growth; the closer it is to 0, the average seedling growth; and the closer it is to -1, the worse the seedling growth.

[0059] Step S40: Generate the integrity commitment hash corresponding to the original feature value and the evidence storage hash corresponding to the fused feature value, and record the evidence storage transaction on the blockchain using the integrity commitment hash and the evidence storage hash.

[0060] In this embodiment, the integrity commitment hash refers to the cryptographic hash value generated by all original feature values, the calculated confidence weights, and the timestamp, ensuring the integrity of the original feature values ​​used to calculate the fused feature value. The evidence storage hash refers to the cryptographic hash value generated by the processed fused feature value G and the timestamp t, ensuring the immutability of the fused feature value.

[0061] Specifically, all original feature values, corresponding confidence weights, and timestamps are serialized, and the serialized data is hashed to generate an integrity commitment hash for multi-source heterogeneous data; and the fused feature values ​​are amplified, rounded, and combined with the timestamps, and the combined data is hashed to generate the evidence hash.

[0062] For example, integrity commitment hash .

[0063] Where, P, , These are the original eigenvalues; , , The corresponding confidence weights; For timestamps.

[0064] Evidence hash of fused feature value (G) .

[0065] in, To Amplifying and rounding the value to an integer can avoid the complexity of putting floating-point numbers on the chain.

[0066] The generated integrity commitment hash Evidence hash and timestamp Together they constitute a notarized transaction The notarized transaction is sent to the blockchain network and recorded in a block after consensus is reached.

[0067] Compared to the single hash scheme that only generates a hash value for the original data, the dual hash structure proposed in this application is not simply an increase in quantity, but rather the construction of a completely new data trust storage paradigm. It can achieve functional separation (integrity commitment and integrated feature value storage) and verification hierarchy (rapid verification and in-depth auditing), achieving better verification efficiency and clearer responsibility definition than the single hash scheme, thereby systematically improving the commercial feasibility and technological advancement of the seedling asset data management solution.

[0068] Specifically, during rapid verification, the verifier (such as a financial institution) can quickly and lightweightly verify the authenticity of the seedling's core status indicator (fusion feature value G) by comparing the on-chain evidence hash with the calculated new hash, without touching any original data, thereby improving the efficiency of high-frequency business verification. In the event of a dispute or when in-depth auditing is required, the auditor can use the integrity commitment hash to perform a complete and non-repudiable integrity verification of all original feature values, weights, and timestamps stored off-chain. This separate verification mechanism solves the "one-size-fits-all" verification mode of single-hash schemes, achieving optimized verification efficiency and refined verification granularity while ensuring ultimate trustworthiness.

[0069] If the evidence hash verification fails, but the integrity commitment hash verification passes, it can be inferred that the problem lies in the fusion calculation process rather than the data source, thus assigning responsibility to the calculation stage. If the integrity commitment hash verification fails, it indicates that the original data has been tampered with. This fine-grained responsibility identification capability is not available in single-hash schemes and can provide a basis for subsequent dispute attribution and trust clarification.

[0070] In the seedling asset preservation method proposed in this application, firstly, this application collects multi-source heterogeneous data on seedlings and extracts multiple original feature values ​​of different dimensions from it, transforming the original, physical-level sensor data into original feature values ​​that can directly quantify the physiological growth state of seedlings. Furthermore, a dynamic confidence weight based on uncertainty measurement is introduced to weight and fuse the original feature values, effectively suppressing distortion or anomalies in single data sources caused by factors such as equipment aging and environmental interference. This dynamically filters and fuses the most reliable data sources, thereby outputting a highly reliable fused feature value. This ensures that the object of on-chain preservation is high-quality information that has undergone reliable processing, rather than original, unreliable readings, guaranteeing the authenticity and accuracy of the data from the source.

[0071] Secondly, this application reduces the amount of data stored on-chain by several orders of magnitude by generating and uploading lightweight double-hash data (integrity commitment hash and evidence storage hash), greatly saving storage costs and network resources. This effectively solves the technical problems of high on-chain storage costs and low data processing efficiency caused by uploading all data to the blockchain in existing technologies. Simultaneously, the collision-free nature of cryptographic hashes ensures that any tampering with off-chain data can be effectively detected, achieving a balance between low cost and high reliability.

[0072] In one embodiment, the method further includes: storing the multi-source heterogeneous data in a distributed storage system, and determining the content identifier corresponding to the multi-source heterogeneous data in the distributed storage system. Here, the content identifier (CID) is a unique and fixed identifier generated by a cryptographic hash function based on the file content itself in a content-addressable distributed storage system. Any minor change to the content will result in a significant change to the CID.

[0073] Step S40 involves recording the integrity commitment hash and the evidence storage hash together as an evidence storage transaction on the blockchain, including: The integrity commitment hash, the evidence storage hash, and the content identifier are used to form an evidence storage transaction record on the blockchain.

[0074] For example, evidence storage transactions .

[0075] The seedling asset preservation method proposed in this application securely, reliably, and cost-effectively stores massive amounts of raw data that are not suitable for direct blockchain storage off-chain, and obtains a content identifier (CID) for locating and verifying the data on-chain, thereby achieving data separation and solving the problem of high on-chain storage costs.

[0076] In one embodiment, such as Figure 2 As shown, the method further includes: In response to a business verification request from a verifier, a zero-knowledge proof is generated on a private blockchain based on the original feature values ​​as private inputs using a proof generation algorithm. The proof circuit corresponding to the proof generation algorithm is configured to generate a zero-knowledge proof when the integrity commitment hash and fused feature value calculated from the private inputs are consistent with the integrity commitment hash and fused feature value stored on the blockchain. The zero-knowledge proof is used to enable the verifier to verify the business verification request on a public blockchain.

[0077] In this embodiment, the business verification request refers to a request initiated by an external verification party (such as a bank, insurance company, or regulatory agency) to prove that the seedling asset status meets specific business conditions (such as a health level greater than a threshold). The private blockchain refers to a private computing environment or offline environment controlled by the seedling owner, capable of securely processing sensitive raw data. Private input, also known as witness, is all the confidential data owned by the proving party (seedling owner) used to generate the proof, which must be kept secret. Specifically, it may include the original feature values ​​P, W, M, and confidence weights. , , And timestamps. A proof generation algorithm refers to an algorithm in a zero-knowledge proof system (such as zk-SNARK) that receives private input and public input (threshold). On-chain evidence storage ), run the proof circuit, and output a zero-knowledge proof ( Zero-knowledge proofs A statement is a cryptographic string that convinces the verifier that a statement is true without revealing any secret information about why it is true. A proof circuit is an arithmetic logic program described in a circuit language (such as R1CS) that defines the complete statement that needs to be proven.

[0078] In this embodiment, the proposition that the seedling owner needs to prove, as defined by the proof circuit, is: .

[0079] In the formula, The threshold set by the external verification party in the business smart contract for the health status of the seedlings, such as... =0.6, then A value of 0.6 indicates that the seedlings are growing healthily.

[0080] During integrity verification, the proof circuit internally recalculates the integrity commitment hash using the same private input. ,verify Is it equal to the integrity commitment hash stored on-chain? This ensures that the original data has not been tampered with.

[0081] When performing business logic verification, it is proven that the circuit internally recalculates the fused feature values ​​using the same private input. , ,verify Is it related to on-chain evidence storage? The values ​​are consistent.

[0082] Only when both integrity verification and business logic verification pass can the proof generation algorithm generate a valid zero-knowledge proof. : .

[0083] Furthermore, the verifier can verify the input... , , and proof Run a lightweight verification algorithm Perform zero-knowledge proof verification: .

[0084] If returned If so, the corresponding business logic will be triggered, such as bank loan approval or approval by the competent authority, and the verification record will be uploaded to the blockchain.

[0085] In the seedling asset preservation method proposed in this application embodiment, firstly, this application can generate a zero-knowledge proof without disclosing any original feature values ​​(P, W, M, etc.) to the verification party. This enables nursery owners to prove their business conditions to verification bodies such as financial institutions and regulators (e.g., This has been satisfied, while ensuring the absolute confidentiality of its core growth data (such as precise growth status and geographic location details).

[0086] Secondly, this application will include the business conditions ( ) is encoded in the proof circuit and linked to data integrity verification ( = This approach involves binding data together. The verifier can obtain a "true / false" conclusion by running a publicly available and definitive verification algorithm, and trigger business operations accordingly. This eliminates the need for the verifier to invest resources in complex data authenticity investigations; they only need to trust mathematics and code. This significantly reduces trust costs, operational costs, and moral hazards in financial transactions, achieving a paradigm shift in business processes.

[0087] In one embodiment, a computer storage medium is provided that stores executable instructions that, when executed by a processor, cause the processor to perform the steps in the above method embodiments.

[0088] In one embodiment, an electronic device is also provided, including one or more processors; and a memory storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the steps in the above method embodiments.

[0089] In one embodiment, such as Figure 3 The diagram illustrates the structure of an electronic device used to implement an embodiment of this application. The electronic device includes a central processing unit (CPU) 101, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 102 or a program loaded from a storage portion 108 into a random access memory (RAM) 103. The RAM 103 also stores various programs and data required for the operation of the electronic device 100. The CPU 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0090] The following components are connected to I / O interface 105: an input section 106 including a keyboard, mouse, etc.; an output section 107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 108 including a hard disk, etc.; and a communication section 109 including a network interface card such as a LAN card, modem, etc. The communication section 109 performs communication processing via a network such as the Internet. A drive 110 is also connected to I / O interface 105 as needed. A removable medium 111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 110 as needed so that computer programs read from it can be installed into storage section 108 as needed.

[0091] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer-readable medium carrying instructions that, in such embodiments, can be downloaded and installed from a network via communication section 109, and / or installed from removable medium 111. When the instructions are executed by central processing unit (CPU) 101, the various method steps described in this application are performed.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0093] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, any of the embodiments or implementations claimed above can be used in any combination. The information disclosed in this background section is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

Claims

1. A method for documenting seedling assets, characterized in that, The method includes: Collect multi-source heterogeneous data of seedlings and extract multiple original feature values ​​of different dimensions from the multi-source heterogeneous data. The original feature values ​​are used to quantitatively characterize the physiological growth status of seedlings. Based on the uncertainty measure of each original feature value in the current time period, the confidence weight of each original feature value is calculated. Based on the confidence weights of each original feature value, multiple original feature values ​​are weighted and fused to obtain a fused feature value. Generate the integrity commitment hash corresponding to the original feature value and the evidence storage hash corresponding to the fused feature value, and record the evidence storage transaction on the blockchain using the integrity commitment hash and the evidence storage hash.

2. The method for storing seedling assets according to claim 1, characterized in that, The multi-source heterogeneous data includes satellite remote sensing data, ground sensor data, and UAV lidar point cloud data. The extraction of multiple original feature values ​​of different dimensions from the multi-source heterogeneous data, used to quantitatively characterize the physiological growth state of the seedlings, includes: Near-infrared and red light reflectance are extracted from the satellite remote sensing data, and a photosynthetic activity index is calculated based on the near-infrared and red light reflectance. The photosynthetic activity index is used to characterize the photosynthetic activity of seedlings. A set of parameters including plant canopy temperature, air temperature, humidity and soil electrical conductivity is extracted from the ground sensor data, and a water stress index is calculated based on the parameter set. The water stress index is used to characterize the water status of seedlings. The three-dimensional spatial structure information of the seedlings is extracted from the point cloud data of the UAV lidar, and the morphological formation index is calculated based on the three-dimensional spatial structure information. The morphological formation index is used to characterize the morphological growth vitality of the seedlings.

3. The method for storing seedling assets according to claim 1, characterized in that, Before calculating the confidence weight of each original feature value based on the uncertainty measure of each original feature value in the current time period, the following steps are included: Obtain the time series data of each original feature value within the current time period, and calculate the corresponding information entropy and standard deviation based on the time series data; Based on the ratio of information entropy to standard deviation corresponding to each original feature value, the uncertainty measure of each original feature value in the current time period is calculated.

4. The method for storing seedling assets according to claim 1, characterized in that, The confidence weights of each original feature value are calculated based on the uncertainty measure of each original feature value in the current time period, including: The confidence weight of each original feature value is calculated based on the uncertainty measure of each original feature value in the current time period and the sum of the reciprocals of the squares of the uncertainty measures of all original feature values ​​in the current time period.

5. The method for storing seedling assets according to claim 1, characterized in that, The weighted fusion of multiple original feature values ​​based on the confidence weights of each original feature value to obtain fused feature values ​​includes: Each original feature value is multiplied by its corresponding confidence weight. The original feature values ​​include the photosynthetic activity index, which characterizes the photosynthetic activity of seedlings; the water stress index, which characterizes the water status of seedlings; and the morphogenesis index, which characterizes the morphological growth activity of seedlings. The product of the photosynthetic activity index, the negative value of the product of the water stress index, and the product of the morphogenesis index are summed to obtain the fusion characteristic value. The range of the fusion characteristic value is then limited to the interval (-1, 1) by the hyperbolic tangent function.

6. The method for storing seedling assets according to claim 1, characterized in that, The generation of the integrity commitment hash corresponding to the original feature value and the evidence storage hash corresponding to the fused feature value includes: All original feature values, corresponding confidence weights, and timestamps are serialized, and hash values ​​are calculated on the serialized data to generate an integrity commitment hash for multi-source heterogeneous data; and The fused feature value is amplified and rounded, then combined with the timestamp, and the combined data is hashed to generate the evidence hash.

7. The method for storing seedling assets according to claim 1, characterized in that, The method further includes: The multi-source heterogeneous data is stored in a distributed storage system, and the content identifier corresponding to the multi-source heterogeneous data in the distributed storage system is determined. The step of recording the integrity commitment hash and the evidence storage hash as evidence storage transactions on the blockchain includes: The integrity commitment hash, the evidence storage hash, and the content identifier are used to form an evidence storage transaction record on the blockchain.

8. The method for storing seedling assets according to any one of claims 1 to 7, characterized in that, The method further includes: In response to a business verification request from a verifier, a zero-knowledge proof is generated on a private blockchain based on the original feature values ​​as private inputs using a proof generation algorithm. The proof circuit corresponding to the proof generation algorithm is configured to generate a zero-knowledge proof when the integrity commitment hash and fused feature value calculated from the private inputs are consistent with the integrity commitment hash and fused feature value stored on the blockchain. The zero-knowledge proof is used to enable the verifier to verify the business verification request on a public blockchain.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the seedling asset preservation method as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that, The storage medium stores executable instructions, which, when executed by a processor, cause the processor to perform the seedling asset preservation method as described in any one of claims 1 to 8.

Images